The present disclosure relates to a carotid artery diagnostic method, and more particularly to a method for diagnosing an abnormal symptom of a carotid artery using one or more artificial neural networks. A carotid ultrasound diagnostic method capable of being executed in a computer system capable of reading a carotid ultrasound video image includes extracting a carotid artery blood vessel image from an input or received carotid ultrasound video image or a carotid ultrasound video image accessed from a storage unit using a pre-trained artificial neural network, and diagnosing whether a carotid artery for the extracted carotid artery blood vessel image is normal using a pre-trained artificial neural network and displaying and outputting a diagnosis result.

One embodiment is directed to a system comprising a head-mounted member removably coupleable to the user's head; one or more electromagnetic radiation emitters coupled to the head-mounted member and configured to emit light with at least two different wavelengths toward at least one of the eyes of the user; one or more electromagnetic radiation detectors coupled to the head-mounted member and configured to receive light reflected after encountering at least one blood vessel of the eye; and a controller operatively coupled to the one or more electromagnetic radiation emitters and detectors and configured to cause the one or more electromagnetic radiation emitters to emit pulses of light while also causing the one or more electromagnetic radiation detectors to detect levels of light absorption related to the emitted pulses of light, and to produce an output that is proportional to an oxygen saturation level in the blood vessel.

Provided is an ultrasound diagnostic apparatus including an ultrasound probe, an imaging section that images the subject on the basis of a reception signal output from the ultrasound probe to generate an ultrasound image, an image analysis section that performs image analysis using the ultrasound image, a movement detection sensor that detects and outputs a movement of the ultrasound probe as a detection signal, a movement amount calculation section that calculates a movement amount of the ultrasound probe in a case where an imaging inspection portion that is currently being imaged among a plurality of inspection portions of the subject is inspected, using the detection signal output from the movement detection sensor, and a portion discrimination section that discriminates the imaging inspection portion on the basis of an image analysis result in the image analysis section and the movement amount calculated by the movement amount calculation section.

The ultrasonic imaging apparatus includes: a display; a probe configured to acquire an ultrasound signal of a heart; and a controller configured to generate an ultrasound image of the heart based on the ultrasound signal, control the display to display the ultrasound image of the heart, and determine an exercise cycle of the heart based on the movement of at least one valve included in the ultrasound image, wherein the ultrasound image comprises an atrium and a ventricle of the heart.

An ultrasound probe (104) includes a probe head (134). The probe head includes a transducer array (136) with a transducing surface (137), an instrument guide (142), and a light source (140). A method includes emitting a light beam, from a light source disposed on and adjacent to a transducer array of an ultrasound imaging probe, in a direction opposite of a transducing surface of the transducer array, at an inside wall of a cavity of a subject or object. A laparoscopic ultrasound imaging probe includes a shaft, a body, an articulating member that couples the probe head, and a handle coupled to the elongate shaft. The articulating probe head includes a transducer array that generates an ultrasound signal that traverses an image plane of the transducer array, an instrument guide, and a light source arranged to emit light in a direction opposite of the image plane.

An ultrasound probe and ultrasound diagnostic apparatus that achieve high transmission/reception sensitivity and wide frequency band are provided. The ultrasound probe includes a pMUT array in which a plurality of pMUTs are arranged. The pMUTs include first pMUTs for ultrasound transmission and pMUTs for ultrasound wave reception having a structure different from that of the first pMUTs. The cell region of each first pMUT and the cell region of each second pMUT are separated from each other in the ultrasound wave radiation plane.

An ultrasound imaging system and method includes acquiring volumetric Doppler data, identifying a first subset of the volumetric Doppler data with turbulent characteristics, identifying a second subset of the volumetric Doppler data with non-turbulent characteristics, and generating a volume-rendered image based at least in part on the volumetric Doppler data. Generating the volume-rendered image includes rendering a turbulent flow volume based on the first subset in a first color, rendering a non-turbulent flow volume based on the second subset in two or more colors that are different than the first color. Generating the volume-rendered image includes rendering an outer perimeter of the non-turbulent flow volume with a higher opacity than a central region of the non-turbulent flow volume. The ultrasound imaging system and method includes displaying the volume-rendered image.

A system for aiding a user to perform a medical ultrasound examination comprises a memory comprising instruction data representing a set of instructions; a processor; and a display. The processor is configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to: i) receive a real-time sequence of ultrasound images captured by an ultrasound probe during the medical ultrasound examination; ii) use a model trained using a machine learning process to take an image frame in the real-time sequence of ultrasound images as input, and output a predicted relevance of one or more image components in the image frame to the medical ultrasound examination being performed; and iii) highlight to the user, in real-time on the display, image components that are predicted by the model to be relevant to the medical ultrasound examination, for further consideration by the user.

An ultrasound diagnostic apparatus (1), which performs a compression test with respect to at least two points of a popliteal vein and a common femoral vein of a subject, includes an ultrasound probe (15), an image acquisition unit (8) that acquires an ultrasound image by transmitting an ultrasound beam toward the subject from the ultrasound probe (15), a vein detection unit (9) that detects the popliteal vein included in the ultrasound image, and an operation guiding unit (10) that guides a user, in a case where the compression test of the popliteal vein is performed, to operate the ultrasound probe such that the ultrasound probe (15) is positioned at a position in which only one popliteal vein is included in the ultrasound image based on the number of the popliteal veins detected by the vein detection unit (9).

A device for a mobile ultrasound unit having an ultrasound probe includes a trained orientation neural network and a result converter. The trained orientation neural network receives a non-canonical image of a body part from the mobile ultrasound unit and generates a transformation transforming between a position and rotation associated with a canonical image and a position and rotation associated with the non-canonical image. The result converter converts the transformation into position and/or rotation instructions of the probe and displays the position and/or rotation instructions to a user to change the position and/or rotation of the probe.